Imaging is a discipline of medicine that uses different modalities of images of human body acquired by a set of equipment and methods to reach in a fast and reliable way the identification of diverse diseases.
Imaging comprises the making of all types of diagnostic and therapeutic exams in which equipment for reproducing images of the body are used, a specialty that has provided an unexpected contribution to the progress and development of health sciences. Nowadays different modalities of human body images are used, which are acquired by using a set of equipment and methods such as: ultrasound, computed axial tomography, nuclear magnetic resonance, conventional and digital radiology, for achieving in a fast and reliable way the identification of different diseases, becoming indispensable tools for the proper and qualified care of patients.
However, it is clear that the benefit obtained from imaging in favor of the health of patients largely depends on the ability of correctly interpret data provided by medical images, regardless of said interpretation is carried out by manual or direct methods (that is, by an expert) by interactive methods, by semi-automatic methods (those in which there is partial intervention of an expert and also computational techniques are applied) or by automatic methods (where the analysis is performed completely through computational techniques).
In addition, interpretation of medical images may include different objectives, including: i) measurement of any property of the input image, so that the result is a scalar or vector; ii) definition of an image as normal or abnormal without the need to identify a specific region within the image; and iii) division of an image into a set of different regions based on a similarity measure, in which case there may be a generic segmentation where the objective is to produce descriptions of the content of an image, or a segmentation that involves the detection and localization of all regions of an image that share a common characteristic.
However, specifically regarding segmentation and quantification of medical images by manual or direct methods, such task generally turns out to be wasteful and subject to inter and intra-observer variability, a fact that has motivated the development of several computational techniques for estimating and discriminating the area of different regions present in the images to be interpreted. However, the anatomical diversity of the patients affects the result of these methods, reason why the intervention of an evaluator is generally necessary to make corrections to the result, being very extensive in some cases, and therefore subtracting reliability for its diagnostic use.
On the other hand, interactive methods assist the expert in the differentiation task by facilitating the tracing of contours that define regions with exclusive content from one or another tissue to be differentiated (distinguished). In this group are found those methods requiring contours traced by an expert user to define the different regions of interest and where quantification is performed taking advantage of the contours given by the user and counting the voxels included within said contours. However, although these methods facilitate the work of the expert, the effort required is still significant and can skew his judgment.
Semi-automatic methods seek to differentiate the region of interest in the image using various schemes for detection of tissues and organs of interest, generally using segmentation global techniques such as ray tracing, region growing and deformable models. However, the existence of strange elements and dependency regarding certain particular anatomical characteristics make necessary the active intervention of the user.
For example, in the scientific paper by Romero et al (2006), a semi-automated detection technique of the external walls of the abdominal cavity for segmentation and differentiation between visceral adipose and subcutaneous tissue is disclosed. Such technique uses a specially designed threshold and two acceptance distance criteria between this, the skin and the intra-peritoneal region, further identifying the muscle tissue to avoid false positives. Unfortunately, said technique presents serious drawbacks when there are discontinuities in the contour of the abdominal region, and consequently the visceral adipose tissue is indistinguishable from the subcutaneous adipose tissue.
Also it is known in the state of the art the proposal by Zhao et al (2006) for detecting the contour of the abdominal internal region in volumetric tomography images, which is based on the creation of radial profiles (rays) from a point located at the geometric center of the rectangle containing the body, so these rays are explored, starting at the external contour of the body towards the center, until finding the first discontinuity corresponding to the adipose tissue, thereby obtaining a candidate contour point. Then, the radius of the candidate points is smoothed in order to correct distortion generated by strange elements such as calcifications and discontinuities in the abdominal internal contour.
In turn, the method proposed by Ohshima et al (2008) allows to detect the internal abdominal contour and the intra-peritoneal region contour using two centers of ray generation, fact that allows to evaluate visceral, subcutaneous and intra-muscular adipose tissue in a independently way on computed axial tomography images. However, as the authors themselves point out, said method has a high dependence on specific anatomical characteristics, being seriously affected by the presence of discontinuities in the internal abdominal contour.
On the other hand, patent application WO 2011/139232 discloses an automatic method for the identification of adipose tissue on a set of three-dimensional magnetic resonance images from the abdomen of a patient, and its subsequent segmentation into visceral and subcutaneous adipose tissue. Such method is based on the definition of two-dimensional or three-dimensional graphs with vertices corresponding to abdominal image voxels and edges connecting neighbor vertices.
However, the method disclosed in said reference uses a global approach (graph partitioning) for the differentiation of adipose tissue voxels, which is supported on the assumption of a minimum thickness of subcutaneous adipose tissue around the intra-peritoneal cavity and in the continuity of that region of adipose tissue, in a way that said region is unique and is delimitated by a unique external contour and a unique internal (closed) contour. Such internal contour is of fundamental importance as it defines the initial partition to be used in the optimization of the graph partition. However, in cases where this assumption is not fulfilled, the method of graph partitioning critically fails with unpredictable results because the region of adipose tissue may be delimited by a single contour: the external, or even by a plurality of internal and external contours, preventing the proper selection of an initial partition. Although the disclosed method foresee the occurrence of this problem in cuts at navel height, does not have a mechanism to prevent this problem when the thickness of subcutaneous adipose tissue is particularly narrow and difficult to distinguish from the skin and musculature of the intra-peritoneal region. This weakness is aggravated by the fact that many cases of clinical importance are related to non-obese individuals with an unusually high distribution of visceral adipose tissue, where it is common to find places where the thickness of subcutaneous adipose tissue is minimal and almost imperceptible (much less than 5 mm). Additionally, the presence of strange elements in the abdominal region, as probes or calcifications, can lead to the existence of multiple internal regions within the subcutaneous region or even its fractionation into multiple connected regions (multiple external contours).
Finally, the scientific paper of Mendoza et al (2011) discloses a method to perform the segmentation and quantification of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) using computed axial tomography (CAT) images. This method uses the evaluation of local anatomical invariants on adipose tissue voxels, achieving their differentiation and quantification.
However, even though this document provides an overview of a computational method for segmentation and quantification of SAT and VAT, therein several factors that are necessary to obtain reliable data are not taken into account, including the following:                The differentiation between SAT and VAT tissues is performed over the entire area of the body, which leads to incorrect tissues differentiation due to incomplete removal of the skin after applying the operator of morphological opening, inducing errors during the evaluation of adipose tissue, especially when there are folds in the skin or acquisition artifacts resulting from patient movement;        A central voxel is labeled as visceral tissue only if the number of neighbor voxels labeled as visceral tissue in its neighborhood is greater than or equal to 6, which leads to underestimate the visceral tissue in places nearby to discontinuities of the intra-peritoneal contour;        The initial gap-filling is performed between the application of the morphological opening and closing operator, which increases the probability of filling the body region in an incomplete way, due to the nature of the morphological opening operator by opening regions defined by a closed contour        The initial thresholding is performed using a range that includes only the adipose tissue, which prevents the correct segmentation of the region of the body, particularly when the differentiation of the subcutaneous tissue between the skin and the contour of the intra-peritoneal cavity is difficult, leading to the appearance of discontinuities in the external contour of the segmented subcutaneous tissue that consequently prevent completely recover the body region;        The neighborhood (denoted as M) on which the final SAT and VAT selection criteria are evaluated, has a size of 3×3, which provides insufficient information to accurately determine the type of adipose tissue to which the central voxel belongs, leading to incorrectly differentiate the tissues.        The initial gap-filling and the gap-filling stage where the radius of the traced rays is limited require as a condition, for labeling the voxel under evaluation, that all traced rays intercept an already segmented voxel, which prevents gaps with discontinuities in its contour to be completely filled in, excluding such tissue regions from differentiation.        
Additionally, the document of Mendoza et al (2011) does not concretely describe key aspects such as the distribution and geometry of the rays, their initialization and termination conditions, the information that must be recorded during tracing, the way in which said recorded information must be processed at the end of rays tracing, as well as neither describes the cases, conditions and actions taken based on this.
In view of the above, it is clear that there is a persistent need in the state of the art to develop a computer implemented method that allows to automatically discriminate between two tissues of interest, from a plurality of images, being able to obtain a quantitative valuation of each of those tissues without requiring the intervention of an expert.